A Lightweight Spectral–Spatial Network for Efficient Semantic Segmentation of Ground-Based Hyperspectral Imagery

Ground-based hyperspectral imaging extends remote sensing to road environments by providing material-sensitive spectra for distinguishing visually similar surfaces and objects. However, pixel-wise prediction from high-dimensional spectral cubes is computationally demanding. This study presents a lightweight spectral–spatial network for efficient semantic segmentation of ground-based hyperspectral imagery. A Spectral Token Stem compresses ordered bands into pooled, normalized, and statistical descriptors before high-resolution encoding. CoordSK blocks combine coordinate-aware directional aggregation with adaptive local-scale selection, while a lightweight guided decoder recovers dense predictions from multi-level features. Experiments on the public HyKo2 and HSI-Drive datasets yield Jaccard indices of 0.6670 and 0.8181, respectively, outperforming representative efficient segmentation baselines. The model contains 0.656 million trainable parameters and reaches FP32 batch-one throughputs of 151.49 and 168.21 FPS on an RTX 5090 for the respective benchmarks. In the additional pretrained-model comparison, the network uses less than 0.5% of HSI-Adapter’s total parameters. Ablation studies validate the contributions of spectral tokenization and coordinate-selective mixing, and an input-representation study demonstrates the benefit of retaining full hyperspectral observations over PCA and pseudo-RGB inputs. These results indicate a favorable accuracy–efficiency trade-off for ground-based hyperspectral remote sensing scene understanding.